Amincissement-sans-segmentation et rehaussement des images de niveau de gris par un filtre de chocs utilisant des champs de diffusion Segmentation-Free Thinning and Enhancement of Grayscale Images by Shock Filter and Diffusion Fields
Bibliographic record
Abstract
and key words In the scope of gray-level image processing and understanding, thinning is certainly a central shape descriptor for image analysis and pattern recognition. Enhancement is also an essential tool in facilitating the visual interpretation and understanding of images, especially for noisy and blurry ones. The lack of general unified frameworks necessitates the investigation of these problems in a coherent fashion, using partial differential equations. In this paper, we present a method for thinning and enhancing images by using a shock filter derived from our previously work introduced on enhancement. This new filter incorporates specific diffusion fields and since each such field is characteristic of a given application, it brings a new degree of freedom to the shock filters, in order to address problems of greater practical interests. Probative results on handwritten documents illustrate the performance and efficiency of our model. Other applications have been added in order to highlight its efficiency. Partial Differential Equations (PDE’s), thinning, enhancement, diffusion field, mathematical morphology, grayscale images. traitement du signal 2006_volume 23_numero 1 79
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".